Residual stress prediction of dissimilar metals welding at NPPs using support vector regression

Residual stresses are an important factor in the component integrity and life assessment of welded structures. In this paper, a support vector regression (SVR) method is presented to predict the residual stress for dissimilar metal welding according to various welding conditions. Dissimilar welding joint between nozzle and pipe is regarded in the analyses since it has been known to be highly susceptible to Primary Water Stress Corrosion Cracking (PWSCC) in the primary system of a nuclear power plant (NPP). The residual stress distributions are predicted along two straight paths of a weld zone: a pipe flow path on the inner weld surface and a path connecting two centers of the inner and outer surfaces of a weld zone of a pipe. Four SVR models are developed for four numerical data groups which are split according to the two end section constraints and the two prediction paths and the SVR models are optimized by a genetic algorithm. The SVR models are trained by using a data set prepared for training, optimized by using an optimization data set, and verified by using a test data set independent of the training data and the optimization data. It is known that the SVR models are sufficiently accurate to be used in the integrity evaluation by predicting the residual stress of dissimilar metal welding zones.